Exploring Graph Embedding in Hyperbolic Space for NLP and Semantic Web Applications

Forschungsthema/Bereich

Graph Embedding; NLP; Semantic Web

Typ der Abschlussarbeit

Master

Startzeitpunkt

15.06.2025

Bewerbungsschluss

31.12.2025

Dauer der Arbeit

6 Months

Beschreibung

With the development of Natural Language Processing technologies, semantic web technologies have become increasingly important in representing and organizing knowledge. These structured knowledge graphs are widely used in applications such as question answering, recommender systems, and semantic search. To help machines better understand these graphs, graph embeddings are used to map entities and relations into continuous vector spaces. This serves as a bridge between symbolic knowledge and neural models.

Most existing embedding methods use Euclidean space, which works well for flat or homogeneous graphs. However, real-world graphs, especially in semantic domains like taxonomies, ontologies, and knowledge graphs, often have hierarchical or tree-like structures. These are difficult to represent properly in Euclidean space.
This master’s thesis focuses on learning graph embedding in the Hyperbolic space. We will explore various hyperbolic graph embedding models and compare them with standard graph neural network methods like RGCN.

Your tasks:
- Learn the semantic web fundamentals (RDF, OWL, ontology structures)
- Implement and test hyperbolic spatial graph embedding methods
- Analyze the differences with RGCN embedding methods

Voraussetzung

Voraussetzungen an Studierende

• Interest in knowledge graphs, symbolic AI, or deep learning
• Good knowledge of at Natural Language Processing, Embedding Space, Graph Machine Learning, or Representation Learning
• Familiarity with frameworks such as PyTorch or TensorFlow

Studiengangsbereiche

• Ingenieurwissenschaften
  Elektrotechnik & Informationstechnik
  Informatik
  Mechatronik & Informationstechnik
• Naturwissenschaften und Technik
  Mathematik

Betreuung

Titel, Vorname, Name

M.Sc Nan Liu

Organisationseinheit

Institut für Automation und angewandte Informatik (IAI)

E-Mail Adresse

nan.liu@kit.edu

Link zur eigenen Homepage/Personenseite

Website

Bewerbung per E-Mail

Bewerbungsunterlagen

• Lebenslauf
• Notenauszug

E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an nan.liu@kit.edu


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